Thesis (B.S.)--Massachusetts Institute of Technology, Dept. of Mathematics, 1980. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND SCIENCE. / Bibliography: leaves 50-57. / by Paul S. Heckbert. / B.S.

Yu Ying-Kin. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (leaves 108-116). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgements --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Motivation and Objectives --- p.1 / Chapter 1.2 --- Problem Definition --- p.3 / Chapter 1.3 --- Contributions --- p.6 / Chapter 1.4 --- Related Publications --- p.8 / Chapter 1.5 --- Organization of the Paper --- p.9 / Chapter 2 --- Background --- p.11 / Chapter 2.1 --- Introduction --- p.11 / Chapter 2.2 --- Pose Estimation --- p.12 / Chapter 2.2.1 --- Overview --- p.12 / Chapter 2.2.2 --- Lowe's Method --- p.14 / Chapter 2.2.3 --- The Genetic Algorithm by Hati and Sen- gupta --- p.15 / Chapter 2.3 --- Structure and Motion --- p.17 / Chapter 2.3.1 --- Overview --- p.17 / Chapter 2.3.2 --- The Extended Lowe's Method --- p.20 / Chapter 2.3.3 --- The Extended Kalman Filter by Azarbaye- jani and Pentland --- p.23 / Chapter 3 --- Model-based Pose Tracking Using Genetic Algo- rithms --- p.27 / Chapter 3.1 --- Introduction --- p.27 / Chapter 3.2 --- Overview of the Algorithm --- p.28 / Chapter 3.3 --- Chromosome Encoding --- p.29 / Chapter 3.4 --- The Genetic Operators --- p.30 / Chapter 3.4.1 --- Mutation --- p.30 / Chapter 3.4.2 --- Crossover --- p.31 / Chapter 3.5 --- Fitness Evaluation --- p.31 / Chapter 3.6 --- The Roulette Wheel Proportionate Selection Scheme --- p.32 / Chapter 3.7 --- The Genetic Algorithm Parameters --- p.33 / Chapter 3.8 --- Experiments and Results --- p.34 / Chapter 3.8.1 --- Synthetic Data Experiments --- p.34 / Chapter 3.8.2 --- Real Scene Experiments --- p.38 / Chapter 4 --- Recursive 3D Structure Acquisition Based on Kalman Filtering --- p.42 / Chapter 4.1 --- Introduction --- p.42 / Chapter 4.2 --- Overview of the Algorithm --- p.43 / Chapter 4.2.1 --- Feature Extraction and Tracking --- p.44 / Chapter 4.2.2 --- Model Initialization --- p.44 / Chapter 4.2.3 --- Structure and Pose Updating --- p.45 / Chapter 4.3 --- Structure Updating --- p.46 / Chapter 4.4 --- Pose Estimation --- p.49 / Chapter 4.5 --- Handling of the Changeable Set of Feature Points --- p.52 / Chapter 4.6 --- Analytical Comparisons with Other Algorithms --- p.54 / Chapter 4.6.1 --- Comparisons with the Interleaved Bundle Adjustment Method --- p.54 / Chapter 4.6.2 --- Comparisons with the EKF by Azarbaye- jani and Pentland --- p.56 / Chapter 4.7 --- Experiments and Results --- p.57 / Chapter 4.7.1 --- Synthetic Data Experiments --- p.57 / Chapter 4.7.2 --- Real Scene Experiments --- p.58 / Chapter 5 --- Simultaneous Pose Tracking and Structure Acqui- sition Using the Interacting Multiple Model --- p.63 / Chapter 5.1 --- Introduction --- p.63 / Chapter 5.2 --- Overview of the Algorithm --- p.65 / Chapter 5.2.1 --- Feature Extraction and Tracking --- p.65 / Chapter 5.2.2 --- Model Initialization --- p.66 / Chapter 5.2.3 --- Structure and Pose Updating --- p.66 / Chapter 5.3 --- Pose Estimation --- p.67 / Chapter 5.3.1 --- The Interacting Multiple Model Algorithm --- p.67 / Chapter 5.3.2 --- Design of the Individual EKFs --- p.71 / Chapter 5.4 --- Structure Updating --- p.74 / Chapter 5.5 --- Handling of the Changeable Set of Feature Points --- p.76 / Chapter 5.6 --- Analytical Comparisons with Other EKF-Based Algorithms --- p.77 / Chapter 5.6.1 --- Computation Speed --- p.77 / Chapter 5.6.2 --- Accuracy of the Recovered Pose Sequences --- p.79 / Chapter 5.7 --- Experiments and Results --- p.80 / Chapter 5.7.1 --- Synthetic Data Experiments --- p.80 / Chapter 5.7.2 --- Real Scene Experiments --- p.80 / Chapter 6 --- Empirical Comparisons of the Structure and Mo- tion Algorithms --- p.87 / Chapter 6.1 --- Introduction --- p.87 / Chapter 6.2 --- Comparisons Using Synthetic Data --- p.88 / Chapter 6.2.1 --- Image Residual Errors --- p.88 / Chapter 6.2.2 --- Computation Efficiency --- p.89 / Chapter 6.2.3 --- Accuracy of Recovered Pose Sequences . . --- p.91 / Chapter 6.3 --- Comparisons Using Real Images --- p.92 / Chapter 6.4 --- Summary --- p.97 / Chapter 7 --- Future Work --- p.99 / Chapter 8 --- Conclusion --- p.101 / Chapter A --- Kalman Filtering --- p.103 / Bibliography --- p.107

Our method can be naturally applied in the application of manga stylization rendering. We propose a novel colorization technique that propagates color over manga regions exhibiting pattern-continuity as well as intensity-continuity. The proposed method works effectively on colorizing black-and-white manga which contains intensive amount of strokes, hatching, halftoning and screening. Once the user scribbles on the drawing, a local, statistical based pattern feature obtained with Gabor wavelet filters is applied to measure the pattern-continuity. The boundary is then propagated by the level set method that monitors the pattern-continuity. Regions with open boundaries or multiple disjointed regions with similar patterns can be sensibly segmented by a single scribble. With the segmented regions, various colorization techniques can be applied to replace colors, colorize with stroke preservation, or even convert pattern to shading. Based on the observation of the manga features, we then propose a framework to generate manga-style backgrounds from real photographs. It frees manga artists from the tedious and time-consuming background production. To mimic how manga artists produce the tidy background, it composes of two major steps, the line drawing and the screen laying. A line importance model is proposed to simplify and embolden lines in a stylish way. During the screen laying, texture analysis is utilized to automatically match the regions in photographs with screens in the database. Two types of screening mechanisms are proposed for matching shading & texture as well as the high-level structures. / Segmentation has always been a crucial and challenging topic in various computer vision and graphics applications. Information from image features can be incorporated into image segmentation techniques to make the process more accurate and robust. This dissertation focuses on encoding these feature information into the image processing procedures, including image segmentation and manga stylization rendering. / The fundamental part of this dissertation includes the discussion on the construction of the speed function, which is important in applying the curve-evolution based image segmentation. We firstly investigate the curvature term in the speed function, and then show how to transform the image segmentation problem into an interface propagating problem. We propose two formulations to enhance the speed function in level set methods, in order to tackle the segmentation problem of tagged MR images. First, a relaxation factor is introduced, aiming at relaxing the boundary condition when the boundary is unclear or blurry. Second, in order to incorporate human visual sensitive information from the image, a simple and general model is introduced to incorporate shape, texture and color features. By further extending this model, we present a unified approach for segmenting and tracking of the high-resolution color anatomical Chinese Visible Human (CVH) data. The underlying relationship of these two applications relies on the proposed variational framework for the speed function. Our proposed method can be used to segment the first slice of the volume data. Then based on the extracted boundary on the first slice, our method can also be adapted to track the boundary of the homogeneous organs among the subsequent serial images. In addition to the promising segmentation results, the tracking procedure requires only a small amount of user intervention. / Qu Yingge. / "July 2007." / Advisers: Heng Pheng Ann; Wong Tien Tsin. / Source: Dissertation Abstracts International, Volume: 69-01, Section: B, page: 0433. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (p. 107-120). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract in English and Chinese. / School code: 1307.

稀疏性在最近的圖像恢復技術發展中起到了重要作用。在這個碩士研究中，我們專注於兩種通過信號稀疏性假設相聯繫的圖像恢復問題。具體來講，在第一個圖像恢復問題中，信號本身在某些變換域是稀疏的，例如小波變換。在本研究的第二部分，信號並非傳統意義上的稀疏，但它可以用很少的幾個參數來表示--亦即信號具有稀疏的表示。我們希望通過講述一個「雙城記」，聯繫起這兩個稀疏圖像重建問題。 / 在第二章中，我們提出了一種創新的算法框架，用於解決信號稀疏假設下的圖像恢復問題。重建圖像的目標函數，由一個數據保真項和`1正則項組成。然而，我們不是直接估計重建的圖像，而是專注於如何獲得重建的這個過程。我們的策略是將這個重建過程表示成基本閾值函數的線性組合（LET）：這些線性係數可以通過最小化目標函數解得。然後，可以更新閾值函數并迭代這個過程（i-LET）。這種線性參數化的主要優點是可以大幅降低問題的規模-每次我們只需解決一個線性係數維度大小的優化問題（通常小於十），而不是整個圖像大小的問題。如果閾值函滿足一定的條件，迭代LET算法可以保證全局的收斂性。多個測試圖像在不同噪音水平和不同卷積核類型的測試清楚地表明，我們提出的框架在所需運算時間和迭代循環次數方面，通常超越當今最好水平。 / 在第三章中，我們擴展了有限創新率採樣框架至某一種特定二維曲線。我們用掩模函數的解來間接定義這個二維曲線。這裡，掩模函數可以表示為有限數目的正弦信號加權求和。因此，從這個角度講，我們定義的二維曲線具有「有限創新率」（FRI）。由於與定義曲線相關聯的指示器圖像沒有帶寬限制，因而根據經典香農採樣定理，不能在有限數量的採樣基礎上獲得完全重建。然而，我們證明，仍然可以設計一個針對指示器圖像採樣的框架，實現完美重構。此外，對於這一方法的空間域解釋，使我們能夠拓展嚴格的FRI曲線模型用於描述自然圖像的邊緣，可以在各種圖像處理的問題中保持圖像的邊緣。我們用一個潛在的在圖像上採樣中的應用作為示例。 / Sparsity has played an important role in recent developments of various image restoration techniques. In this MPhil study, we focus on two different types of image restoration problems, which are related by the sparsity assumptions. Specifically, in the first image restoration problem, the signal (i.e. the restored image) itself is sparse in some transformation domain, e.g. wavelet. While in the second part of this study, the signal is not sparse in the traditional sense but that it can be parametrized with a few parameters hence having a sparse representation. Our goal is to tell a "tale of two cities" and to show the connections between the two sparse image restoration problems in this thesis. / In Chapter 2, we proposed a novel algorithmic framework to solve image restoration problems under sparsity assumptions. As usual, the reconstructed image is the minimum of an objective functional that consists of a data fidelity term and an ℓ₁ regularization. However, instead of estimating the reconstructed image that minimizes the objective functional directly, we focus on the restoration process that maps the degraded measurements to the reconstruction. Our idea amounts to parameterizing the process as a linear combination of few elementary thresholding functions (LET) and solve for the linear weighting coefficients by minimizing the objective functional. It is then possible to update the thresholding functions and to iterate this process (i-LET). The key advantage of such a linear parametrization is that the problem size reduces dramatically--each time we only need to solve an optimization problem over the dimension of the linear coefficients (typically less than 10) instead of the whole image dimensio . With the elementary thresholding functions satisfying certain constraints, global convergence of the iterated LET algorithm is guaranteed. Experiments on several test images over a wide range of noise levels and different types of convolution kernels clearly indicate that the proposed framework usually outperform state-of-theart algorithms in terms of both CPU time and number of iterations. / In Chapter 3, we extended the sampling framework for signals with finite rate of innovation to a specific class of two-dimensional curves, which are defined implicitly as the roots of a mask function. Here the mask function has a parametric representation as weighted summation of a finite number of sinusoids, and therefore, has finite rate of innovation [1]. The associated indicator image of the defined curve is not bandlimited and cannot be perfectly reconstructed based on the classical Shannon's sampling theorem. Yet, we show that it is possible to devise a sampling scheme and have a perfect reconstruction from finite number of (noiseless) samples of the indicator image with the annihilating filter method (also known as Prony's method). Robust reconstruction algorithms with noisy samples are also developed. Furthermore, the new spatial domain interpretation of the annihilating filter enables us to generalize the exact FRI curve model to characterize edges of a natural image. We can impose the annihilation constraint to preserve edges in various image processing problems. We exemplified the effectiveness of the annihilation constraint with a potential application in image up-sampling. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Pan, Hanjie. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2013. / Includes bibliographical references (leaves 69-74). / Abstracts also in Chinese. / Acknowledgments --- p.iii / Abstract --- p.vii / Contents --- p.xii / List of Figures --- p.xv / List of Tables --- p.xvii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Sampling Sparse Signals --- p.1 / Chapter 1.2 --- Thesis Organizations and Contributions --- p.3 / Chapter 2 --- An Iterated Linear Expansion of Thresholds for ℓ₁-based Image Restoration --- p.5 / Chapter 2.1 --- Introduction --- p.5 / Chapter 2.1.1 --- Problem Description --- p.5 / Chapter 2.1.2 --- Approaches to Solve the Problem --- p.6 / Chapter 2.1.3 --- Proposed Approach --- p.8 / Chapter 2.1.4 --- Organization of the Chapter --- p.9 / Chapter 2.2 --- Basic Ingredients --- p.9 / Chapter 2.2.1 --- Iterative Reweighted Least Square Methods --- p.9 / Chapter 2.2.2 --- Linear Expansion of Thresholds (LET) --- p.11 / Chapter 2.3 --- Iterative LET Restoration --- p.15 / Chapter 2.3.1 --- Selection of i-LET Bases --- p.15 / Chapter 2.3.2 --- Convergence of the i-LET Scheme --- p.16 / Chapter 2.3.3 --- Examples of i-LET Bases --- p.18 / Chapter 2.4 --- Experimental Results --- p.23 / Chapter 2.4.1 --- Deconvolution with Decimated Wavelet Transform --- p.24 / Chapter 2.4.2 --- Deconvolution with Redundant Wavelet Transform --- p.28 / Chapter 2.4.3 --- Algorithm Complexity Analysis --- p.29 / Chapter 2.4.4 --- Choice of Regularization Weight λ --- p.30 / Chapter 2.4.5 --- Deconvolution with Cycle Spinnings --- p.30 / Chapter 2.5 --- Summary --- p.31 / Chapter 3 --- Sampling Curves with Finite Rate of Innovation --- p.33 / Chapter 3.1 --- Introduction --- p.33 / Chapter 3.2 --- Two-dimensional Curves with Finite Rate of Innovation --- p.34 / Chapter 3.2.1 --- FRI Curves --- p.34 / Chapter 3.2.2 --- Interior Indicator Image --- p.35 / Chapter 3.2.3 --- Acquisition of Indicator Image Samples --- p.36 / Chapter 3.3 --- Reconstruction of the Annihilable Curves --- p.37 / Chapter 3.3.1 --- Annihilating Filter Method --- p.37 / Chapter 3.3.2 --- Relate Fourier Transform with Spatial Domain Samples --- p.39 / Chapter 3.3.3 --- Reconstruction of Annihilation Coe cients --- p.39 / Chapter 3.3.4 --- Reconstruction with Model Mismatch --- p.42 / Chapter 3.3.5 --- Retrieval of the Annihilable Curve Amplitudes --- p.46 / Chapter 3.4 --- Dealing with Non-ideal Low-pass Filtered Samples --- p.48 / Chapter 3.5 --- Generalization of the FRI Framework for Natural Images --- p.49 / Chapter 3.5.1 --- Spatial Domain Interpretation of the Annihilation Equation --- p.50 / Chapter 3.5.2 --- Annihilable Curve Approximation of Image Edges --- p.51 / Chapter 3.5.3 --- Up-sampling with Annihilation Constraint --- p.53 / Chapter 3.6 --- Conclusion --- p.57 / Chapter 4 --- Conclusions --- p.59 / Chapter 4.1 --- Thesis Summary --- p.59 / Chapter 4.2 --- Perspectives --- p.60 / Chapter A --- Proofs and Derivations --- p.61 / Chapter A.1 --- Proof of Lemma 3 --- p.61 / Chapter A.2 --- Proof of Theorem 2 --- p.62 / Chapter A.3 --- Efficient Implementation of IRLS Inner Loop with Matlab --- p.63 / Chapter A.4 --- Derivations of the Sampling Formula (3.7) --- p.64 / Chapter A.5 --- Correspondence between the Spatial and Fourier Domain Samples --- p.65 / Chapter A.6 --- Optimal Post-filter Applied to Non-ideal Samples --- p.66 / Bibliography --- p.69

by Ling Kai Tung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2003. / Includes bibliographical references (leaves 44-45). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.7 / Chapter 2 --- Fundamentals --- p.9 / Chapter 2.1 --- Digital image representation --- p.9 / Chapter 2.2 --- Motion Blur --- p.13 / Chapter 3 --- Methods for Solving Nonlinear Least-Squares Prob- lem --- p.15 / Chapter 3.1 --- Introduction --- p.15 / Chapter 3.2 --- Nonlinear Least-Squares Problem --- p.15 / Chapter 3.3 --- Gauss-Newton-Type Methods --- p.16 / Chapter 3.3.1 --- Gauss-Newton Method --- p.16 / Chapter 3.3.2 --- Damped Gauss-Newton Method --- p.17 / Chapter 3.4 --- Full Newton-Type Methods --- p.17 / Chapter 3.4.1 --- Quasi-Newton methods --- p.18 / Chapter 3.5 --- Constrained problems --- p.19 / Chapter 4 --- Reconstruction of High-Resolution Images from Movie Frames --- p.20 / Chapter 4.1 --- Introduction --- p.20 / Chapter 4.2 --- The Mathematical Model --- p.22 / Chapter 4.2.1 --- The Discrete Model --- p.23 / Chapter 4.2.2 --- Regularization --- p.24 / Chapter 4.3 --- Acquisition of Low-Resolution Movie Frames --- p.25 / Chapter 4.4 --- Experimental Results --- p.25 / Chapter 4.5 --- Concluding Remarks --- p.26 / Chapter 5 --- Constrained Total Least-Squares Computations for High-Resolution Image Reconstruction --- p.31 / Chapter 5.1 --- Introduction --- p.31 / Chapter 5.2 --- The Mathematical Model --- p.32 / Chapter 5.3 --- Numerical Algorithm --- p.37 / Chapter 5.4 --- Numerical Results --- p.39 / Chapter 5.5 --- Concluding Remarks --- p.39 / Bibliography --- p.44

Liu, Ke. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (leaves 41-46). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Related Work --- p.7 / Chapter 2.1 --- Keyword Expansion --- p.7 / Chapter 2.2 --- Content-based Image Search and Visual Expansion --- p.8 / Chapter 3 --- Algorithm --- p.12 / Chapter 3.1 --- Overview --- p.12 / Chapter 3.2 --- Visual Distance Calculation --- p.14 / Chapter 3.2.1 --- Visual Features --- p.15 / Chapter 3.2.2 --- Adaptive Weight Schema --- p.17 / Chapter 3.3 --- Keyword Expansion --- p.18 / Chapter 3.4 --- Visual Query Expansion --- p.22 / Chapter 3.5 --- Image Pool Expansion --- p.24 / Chapter 3.6 --- Textual Feature Combination --- p.26 / Chapter 4 --- Experimental Evaluation --- p.27 / Chapter 4.1 --- Dataset --- p.27 / Chapter 4.2 --- Experiment One: Evaluation with Ground Truth --- p.28 / Chapter 4.2.1 --- Precisions on Different Steps --- p.28 / Chapter 4.2.2 --- Accuracy of Keyword Expansion --- p.31 / Chapter 4.3 --- Experiment Two: User Study --- p.33 / Chapter 5 --- Conclusion --- p.39

Chung, Hin Shun. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2008. / Includes bibliographical references (leaves 40-43). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Lambertian photometric stereo --- p.1 / Chapter 1.2 --- Non-Lambertian photometric stereo --- p.3 / Chapter 1.3 --- Large specular lobe problems --- p.4 / Chapter 2 --- Related Work --- p.9 / Chapter 2.1 --- Lambertian photometric stereo --- p.9 / Chapter 2.2 --- Non-Lambertian photometric stereo --- p.9 / Chapter 2.2.1 --- Analytic models to reconstruct non-Lambertian surface --- p.9 / Chapter 2.2.2 --- Reference object based --- p.10 / Chapter 2.2.3 --- Highlight removal before shape reconstruction --- p.11 / Chapter 2.2.4 --- Polarization based method --- p.12 / Chapter 2.2.5 --- Specularity fitting method --- p.12 / Chapter 2.2.6 --- Photometric stereo with shadow --- p.12 / Chapter 3 --- Our System --- p.13 / Chapter 3.1 --- Estimation of global parameters --- p.14 / Chapter 3.1.1 --- Shadow separation --- p.16 / Chapter 3.1.2 --- Separation edges of shadow and edges of foreground object --- p.16 / Chapter 3.1.3 --- Normal estimation using shadow boundary --- p.20 / Chapter 3.1.4 --- Global parameter estimation and refinement --- p.22 / Chapter 3.2 --- Surface shape and texture reconstruction --- p.24 / Chapter 3.3 --- Single material results --- p.25 / Chapter 4 --- Comparison between Our Method and Direct Specularity Fitting Method --- p.29 / Chapter 4.1 --- Summary of direct specularity fitting method [9] --- p.29 / Chapter 4.2 --- Comparison results --- p.31 / Chapter 5 --- Reconstructing Multiple-Material Surfaces --- p.33 / Chapter 5.1 --- Multiple material results --- p.34 / Chapter 6 --- Conclusion --- p.38 / Bibliography --- p.39 / Chapter A --- Proof of Surface Normal Projecting to Gradient of Cast Shadow Boundary --- p.43

Cheng, Kei Tsi Daniel. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (leaves 45-49). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Background Knowledge --- p.6 / Chapter 2.1 --- Image Restoration using Total Variation Norm --- p.6 / Chapter 2.2 --- An Example of Tight Frame system --- p.10 / Chapter 2.3 --- Sparse and compressed representation --- p.13 / Chapter 2.4 --- Existence of minimizer in convex analysis --- p.16 / Chapter 3 --- Tight Frame Based Minimization --- p.18 / Chapter 3.1 --- Tight Frames --- p.18 / Chapter 3.2 --- Minimization Problems and Algorithms --- p.19 / Chapter 3.3 --- Other Minimization Problems --- p.22 / Chapter 4 --- Algorithm from minimization problem 3 --- p.24 / Chapter 5 --- Algorithm from minimization problem 4 --- p.28 / Chapter 6 --- Convergence of Algorithm 2 --- p.31 / Chapter 6.1 --- Inner Iteration --- p.31 / Chapter 6.2 --- Outer Iteration --- p.33 / Chapter 6.2.1 --- Existence of minimizer --- p.33 / Chapter 7 --- Numerical Results --- p.37 / Chapter 8 --- Conclusion --- p.44

This thesis develops an algorithm for approximate string matching and applies it to the problem of partially occluded object recognition. The algorithm measures the similarity of differing strings by scanning for matching substrings between strings. The length and number of matching substrings determines the amount of similarity. A classification algorithm is developed using the approximate string matching algorithm for the identification and classification of objects. A previously developed method of shape description is used for object representation.

Abstract not available